OpenRaiser/Intern-Atlas huggingface dataset:
Linear Complementary Pair Of Group Codes over Finite Chain Rings
Venue: arXiv cs.IT 2020
Year: 2020

Linear complementary dual (LCD) codes and linear complementary pair (LCP) of codes over finite fields have been intensively studied recently due to their applications in cryptography, in the context of side-channel and fault injection attacks. The security parameter for an LCP of codes $(C,D)$ is defined as the minimum of the minimum distances $d(C)$ and $d(D^\bot)$. It has been recently shown that if $C$ and $D$ are both 2-sided group codes over a finite field, then $C$ and $D^\bot$ are permutation equivalent. Hence the security parameter for an LCP of 2-sided group codes $(C,D)$ is simply $d(C)$. We extend this result to 2-sided group codes over finite chain rings.
Robust Deep Ordinal Regression Under Label Noise
Venue: arXiv cs.LG 2020
Year: 2020

The real-world data is often susceptible to label noise, which might constrict the effectiveness of the existing state of the art algorithms for ordinal regression. Existing works on ordinal regression do not take label noise into account. We propose a theoretically grounded approach for class conditional label noise in ordinal regression problems. We present a deep learning implementation of two commonly used loss functions for ordinal regression that is both - 1) robust to label noise, and 2) rank consistent for a good ranking rule. We verify these properties of the algorithm empirically and show robustness to label noise on real data and rank consistency. To the best of our knowledge, this is the first approach for robust ordinal regression models.
Repeatedly Energy-Efficient and Fair Service Coverage: UAV Slicing (Proactive UAV Network Slicing for URLLC and Mobile Broadband Service Multiplexing)
Venue: arXiv cs.NI 2020
Year: 2020

Unmanned aerial vehicle (UAV) networks are convinced as a significant part of 5G and emerging 6G wireless networks. UAV slicing is a promising proposal of converging different services onto a common UAV network without deploying individual network solution for each type of service. This paper is concerned with UAV slicing for providing energy-efficient and fair service coverage for enhanced mobile broadband (eMBB) users (UEs). Aiming at physically configuring UAV slices, the UAV slicing problem is formulated as a time-dependent mixed-integer-non-convex programming problem with a goal of maximizing all UEs' data rates while minimizing UAVs' total transmit power. To mitigate this challenging problem, we first decompose the original problem into two time-dependent subproblems using a Lyapunov approach. We then derive the procedure of tackling the non-convexity and the mixed-integer property of the subproblems by exploring a successive convex approximate (SCA) method and an alternative optimization scheme, respectively. Based on the derived results, we develop an algorithm with provable performance guarantees to mitigate the two subproblems repeatedly.
Compressing 3DCNNs Based on Tensor Train Decomposition
Venue: arXiv cs.CV 2020
Year: 2020

Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks, e.g., video and 3D point cloud recognition. However, due to the higher dimension of convolutional kernels, the space complexity of 3DCNNs is generally larger than that of traditional two dimensional convolutional neural networks (2DCNNs). To miniaturize 3DCNNs for the deployment in confining environments such as embedded devices, neural network compression is a promising approach. In this work, we adopt the tensor train (TT) decomposition, a straightforward and simple in situ training compression method, to shrink the 3DCNN models. Through proposing tensorizing 3D convolutional kernels in TT format, we investigate how to select appropriate TT ranks for achieving higher compression ratio. We have also discussed the redundancy of 3D convolutional kernels for compression, core significance and future directions of this work, as well as the theoretical computation complexity versus practical executing time of convolution in TT. In the light of multiple contrast experiments based on VIVA challenge, UCF11, and UCF101 datasets, we conclude that TT decomposition can compress 3DCNNs by around one hundred times without significant accuracy loss, which will enable its applications in extensive real world scenarios.
Exploring the Effectiveness of Face-to-face Mixed Reality for Teaching with Chalktalk
Venue: arXiv cs.HC 2020
Year: 2020

Teaching that uses projected presentation media such as slide-shows lacks support for dynamic content whose form and behaviors require live changes during a lecture. Recent software alternatives such as the Chalktalk software platform allow the creation of interactive simulations in arbitrary sequences and combinations within presentations. These more dynamic solutions, however, do not optimize for face-to-face interactions: eye-contact, gaze direction, and concurrent awareness of another person's movements together with the presented content. To explore the extent to which these face-to-face interactions may improve learning and engagement during a lecture, we propose a Mixed Reality (MR) platform that places Chalktalk's behaviors and simulations within a mirrored virtual world environment designed for face-to-face, one-on-one interactions. We compare our system with projected Chalktalk to evaluate its relative effectiveness for learning, retention, and level of engagement.
Bi-Semantic Reconstructing Generative Network for Zero-shot Learning
Venue: arXiv cs.CV 2020
Year: 2020

Many recent methods of zero-shot learning (ZSL) attempt to utilize generative model to generate the unseen visual samples from semantic descriptions and random noise. Therefore, the ZSL problem becomes a traditional supervised classification problem. However, most of the existing methods based on the generative model only focus on the quality of synthesized samples at the training stage, and ignore the importance of the zero-shot recognition stage. In this paper, we consider both the above two points and propose a novel approach. Specially, we select the Generative Adversarial Network (GAN) as our generative model. In order to improve the quality of synthesized samples, considering the internal relation of the semantic description in the semantic space as well as the fact that the seen and unseen visual information belong to different domains, we propose a bi-semantic reconstructing (BSR) component which contain two different semantic reconstructing regressors to lead the training of GAN. Since the semantic descriptions are available during the training stage, to further improve the ability of classifier, we combine the visual samples and semantic descriptions to train a classifier. At the recognition stage, we naturally utilize the BSR component to transfer the visual features and semantic descriptions, and concatenate them for classification. Experimental results show that our method outperforms the state of the art on several ZSL benchmark datasets with significant improvements.
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization
Venue: arXiv cs.LG 2020
Year: 2020

Structured CNN designed using the prior information of problems potentially improves efficiency over conventional CNNs in various tasks in solving PDEs and inverse problems in signal processing. This paper introduces BNet2, a simplified Butterfly-Net and inline with the conventional CNN. Moreover, a Fourier transform initialization is proposed for both BNet2 and CNN with guaranteed approximation power to represent the Fourier transform operator. Experimentally, BNet2 and the Fourier transform initialization strategy are tested on various tasks, including approximating Fourier transform operator, end-to-end solvers of linear and nonlinear PDEs, and denoising and deblurring of 1D signals. On all tasks, under the same initialization, BNet2 achieves similar accuracy as CNN but has fewer parameters. And Fourier transform initialized BNet2 and CNN consistently improve the training and testing accuracy over the randomly initialized CNN.
Implementing the Exponential Mechanism with Base-2 Differential Privacy
Venue: arXiv cs.CR 2020
Year: 2020

Despite excellent theoretical support, Differential Privacy (DP) can still be a challenge to implement in practice. In part, this challenge is due to the concerns associated with translating arbitrary- or infinite-precision theoretical mechanisms to the reality of floating point or fixed-precision. Beginning with the troubling result of Mironov demonstrating the security issues of using floating point for implementing the Laplace mechanism, there have been many reasonable questions raised concerning the vulnerabilities of real-world implementations of DP. In this work, we examine the practicalities of implementing the exponential mechanism of McSherry and Talwar. We demonstrate that naive or malicious implementations can result in catastrophic privacy failures. To address these problems, we show that the mechanism can be implemented exactly for a rich set of values of the privacy parameter $\varepsilon$ and utility functions with limited practical overhead in running time and minimal code complexity. How do we achieve this result? We employ a simple trick of switching from base $e$ to base $2$, allowing us to perform precise base $2$ arithmetic. A short, precise expression is always available for $\varepsilon$, and the only approximation error we incur is the conversion of the base-2 privacy parameter back to base $e$ for reporting purposes. The core base $2$ arithmetic of the mechanism can be simply and efficiently implemented using open-source high precision arithmetic libraries. Furthermore, the exact nature of the implementation lends itself to simple monitoring of correctness and proofs of privacy.
Multistage Graph Problems on a Global Budget
Venue: arXiv cs.CC 2020
Year: 2020

Time-evolving or temporal graphs gain more and more popularity when studying the behavior of complex networks. In this context, the multistage view on computational problems is among the most natural frameworks. Roughly speaking, herein one studies the different (time) layers of a temporal graph (effectively meaning that the edge set may change over time, but the vertex set remains unchanged), and one searches for a solution of a given graph problem for each layer. The twist in the multistage setting is that the solutions found must not differ too much between subsequent layers. We relax on this already established notion by introducing a global instead of the local budget view studied so far. More specifically, we allow for few disruptive changes between subsequent layers but request that overall, that is, summing over all layers, the degree of change is moderate. Studying several classical graph problems (both NP-hard and polynomial-time solvable ones) from a parameterized complexity angle, we encounter both fixed-parameter tractability and parameterized hardness results. Somewhat surprisingly, we find that sometimes the global multistage versions of NP-hard problems such as Vertex Cover turn out to be computationally more tractable than the ones of polynomial-time solvable problems such as Matching.
Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking and Vehicle Re-ID in Multi-Camera Networks
Venue: arXiv cs.CV 2020
Year: 2020

As camera networks have become more ubiquitous over the past decade, the research interest in video management has shifted to analytics on multi-camera networks. This includes performing tasks such as object detection, attribute identification, and vehicle/person tracking across different cameras without overlap. Current frameworks for management are designed for multi-camera networks in a closed dataset environment where there is limited variability in cameras and characteristics of the surveillance environment are well known. Furthermore, current frameworks are designed for offline analytics with guidance from human operators for forensic applications. This paper presents a teamed classifier framework for video analytics in heterogeneous many-camera networks with adversarial conditions such as multi-scale, multi-resolution cameras capturing the environment with varying occlusion, blur, and orientations. We describe an implementation for vehicle tracking and vehicle re-identification (re-id), where we implement a zero-shot learning (ZSL) system that performs automated tracking of all vehicles all the time. Our evaluations on VeRi-776 and Cars196 show the teamed classifier framework is robust to adversarial conditions, extensible to changing video characteristics such as new vehicle types/brands and new cameras, and offers real-time performance compared to current offline video analytics approaches.
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
Venue: arXiv cs.CV 2020
Year: 2020

Spatiotemporal representations learned using 3D convolutional neural networks (CNN) are currently used in state-of-the-art approaches for action related tasks. However, 3D-CNN are notorious for being memory and compute resource intensive as compared with more simple 2D-CNN architectures. We propose to hallucinate spatiotemporal representations from a 3D-CNN teacher with a 2D-CNN student. By requiring the 2D-CNN to predict the future and intuit upcoming activity, it is encouraged to gain a deeper understanding of actions and how they evolve. The hallucination task is treated as an auxiliary task, which can be used with any other action related task in a multitask learning setting. Thorough experimental evaluation shows that the hallucination task indeed helps improve performance on action recognition, action quality assessment, and dynamic scene recognition tasks. From a practical standpoint, being able to hallucinate spatiotemporal representations without an actual 3D-CNN can enable deployment in resource-constrained scenarios, such as with limited computing power and/or lower bandwidth. Codebase is available here: https://github.com/ParitoshParmar/HalluciNet.
Oversampling Log Messages Using a Sequence Generative Adversarial Network for Anomaly Detection and Classification
Venue: arXiv cs.LG 2020
Year: 2020

Dealing with imbalanced data is one of the main challenges in machine/deep learning algorithms for classification. This issue is more important with log message data as it is typically very imbalanced and negative logs are rare. In this paper, a model is proposed to generate text log messages using a SeqGAN network. Then features are extracted using an Autoencoder and anomaly detection is done using a GRU network. The proposed model is evaluated with two imbalanced log data sets, namely BGL and Openstack. Results are presented which show that oversampling and balancing data increases the accuracy of anomaly detection and classification.
Hybrid Style Siamese Network: Incorporating style loss in complementary apparels retrieval
Venue: arXiv cs.CV 2020
Year: 2020

Image Retrieval grows to be an integral part of fashion e-commerce ecosystem as it keeps expanding in multitudes. Other than the retrieval of visually similar items, the retrieval of visually compatible or complementary items is also an important aspect of it. Normal Siamese Networks tend to work well on complementary items retrieval. But it fails to identify low level style features which make items compatible in human eyes. These low level style features are captured to a large extent in techniques used in neural style transfer. This paper proposes a mechanism of utilising those methods in this retrieval task and capturing the low level style features through a hybrid siamese network coupled with a hybrid loss. The experimental results indicate that the proposed method outperforms traditional siamese networks in retrieval tasks for complementary items.
RVSDG: An Intermediate Representation for Optimizing Compilers
Venue: arXiv cs.PL 2020
Year: 2020

Intermediate Representations (IRs) are central to optimizing compilers as the way the program is represented may enhance or limit analyses and transformations. Suitable IRs focus on exposing the most relevant information and establish invariants that different compiler passes can rely on. While control-flow centric IRs appear to be a natural fit for imperative programming languages, analyses required by compilers have increasingly shifted to understand data dependencies and work at multiple abstraction layers at the same time. This is partially evidenced in recent developments such as the MLIR proposed by Google. However, rigorous use of data flow centric IRs in general purpose compilers has not been evaluated for feasibility and usability as previous works provide no practical implementations. We present the Regionalized Value State Dependence Graph (RVSDG) IR for optimizing compilers. The RVSDG is a data flow centric IR where nodes represent computations, edges represent computational dependencies, and regions capture the hierarchical structure of programs. It represents programs in demand-dependence form, implicitly supports structured control flow, and models entire programs within a single IR. We provide a complete specification of the RVSDG, construction and destruction methods, as well as exemplify its utility by presenting Dead Node and Common Node Elimination optimizations. We implemented a prototype compiler and evaluate it in terms of performance, code size, compilation time, and representational overhead. Our results indicate that the RVSDG can serve as a competitive IR in optimizing compilers while reducing complexity.
HistoNet: Predicting size histograms of object instances
Venue: arXiv cs.CV 2020
Year: 2020

We propose to predict histograms of object sizes in crowded scenes directly without any explicit object instance segmentation. What makes this task challenging is the high density of objects (of the same category), which makes instance identification hard. Instead of explicitly segmenting object instances, we show that directly learning histograms of object sizes improves accuracy while using drastically less parameters. This is very useful for application scenarios where explicit, pixel-accurate instance segmentation is not needed, but there lies interest in the overall distribution of instance sizes. Our core applications are in biology, where we estimate the size distribution of soldier fly larvae, and medicine, where we estimate the size distribution of cancer cells as an intermediate step to calculate the tumor cellularity score. Given an image with hundreds of small object instances, we output the total count and the size histogram. We also provide a new data set for this task, the FlyLarvae data set, which consists of 11,000 larvae instances labeled pixel-wise. Our method results in an overall improvement in the count and size distribution prediction as compared to state-of-the-art instance segmentation method Mask R-CNN.
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction
Venue: arXiv cs.CV 2020
Year: 2020

In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest. In the past, NAS was hardly accessible to researchers without access to large-scale compute systems, due to very long compute times for the recurrent search and evaluation of new candidate architectures. The NAS-Bench-101 dataset facilitates a paradigm change towards classical methods such as supervised learning to evaluate neural architectures. In this paper, we propose a graph encoder built upon Graph Neural Networks (GNN). We demonstrate the effectiveness of the proposed encoder on NAS performance prediction for seen architecture types as well an unseen ones (i.e., zero shot prediction). We also provide a new variational-sequential graph autoencoder (VS-GAE) based on the proposed graph encoder. The VS-GAE is specialized on encoding and decoding graphs of varying length utilizing GNNs. Experiments on different sampling methods show that the embedding space learned by our VS-GAE increases the stability on the accuracy prediction task.
An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification
Venue: arXiv cs.MA 2020
Year: 2020

Simulation-based verification is beneficial for assessing otherwise dangerous or costly on-road testing of autonomous vehicles (AV). This paper addresses the challenge of efficiently generating effective tests for simulation-based AV verification using software testing agents. The multi-agent system (MAS) programming paradigm offers rational agency, causality and strategic planning between multiple agents. We exploit these aspects for test generation, focusing in particular on the generation of tests that trigger the precondition of an assertion. On the example of a key assertion we show that, by encoding a variety of different behaviours respondent to the agent's perceptions of the test environment, the agency-directed approach generates twice as many effective tests than pseudo-random test generation, while being both efficient and robust. Moreover, agents can be encoded to behave naturally without compromising the effectiveness of test generation. Our results suggest that generating tests using agency-directed testing significantly improves upon random and simultaneously provides more realistic driving scenarios.
Zero-shot generalization using cascaded system-representations
Venue: arXiv cs.RO 2020
Year: 2020

Deep reinforcement learning has been applied to solve a variety of control problems in isolation. However, the learned latent representations cannot be optimally reused for other analogous tasks and/or control systems without additional training or tuning. In this regard, we propose a novel framework that can be used to learn a single control policy for a whole class of analogous control systems. The framework is abbreviated as CASNET and it leverages the similarities in the designs of analogous control-systems to learn general-purpose abstract system-representations. The framework uses a cascade of recurrent neural networks-based encoders to create these representations which are then fed to a conventional policy network as input. A similar cascade of decoders decodes the output of the policy network to generate system-specific output. We illustrate the effectiveness of this framework on arguably the most significant use-case of DRL: Robotics. In this paper, we use CASNET to learn generalizable control policies for two separate classes of robots: planer-manipulators and crawling robots, using 15+ and 55+ morphologically analogous simulated robots respectively. These robot models encompass the most common design variations used in the real world. Our empirical results using state of the art on and off policy learning algorithms show that on average, CASNET agent achieves zero shot optimal performance (performance equivalent to expert agents trained for individual robot models) on unseen robot models. These results illustrate that the performance of the learned policy is bound the learning algorithm rather than the framework itself. The proposed framework serves a major step towards universal controllers.